Overview

Dataset statistics

Number of variables13
Number of observations15447
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric12

Warnings

Unnamed: 0 has a high cardinality: 15447 distinct values High cardinality
C42_1 is highly correlated with C42_2 and 10 other fieldsHigh correlation
C42_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42_3 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42B_1 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42B_2 is highly correlated with C42_1 and 10 other fieldsHigh correlation
C42B_3 is highly correlated with C42_1 and 9 other fieldsHigh correlation
LNCAP_1 is highly correlated with C42_1 and 10 other fieldsHigh correlation
LNCAP_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
LNCAP_3 is highly correlated with C42_1 and 3 other fieldsHigh correlation
MR49F_1 is highly correlated with C42_1 and 10 other fieldsHigh correlation
MR49F_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
MR49F_3 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42_1 is highly correlated with C42_2 and 10 other fieldsHigh correlation
C42_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42_3 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42B_1 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42B_2 is highly correlated with C42_1 and 10 other fieldsHigh correlation
C42B_3 is highly correlated with C42_1 and 9 other fieldsHigh correlation
LNCAP_1 is highly correlated with C42_1 and 10 other fieldsHigh correlation
LNCAP_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
LNCAP_3 is highly correlated with C42_1 and 3 other fieldsHigh correlation
MR49F_1 is highly correlated with C42_1 and 10 other fieldsHigh correlation
MR49F_2 is highly correlated with C42_1 and 9 other fieldsHigh correlation
MR49F_3 is highly correlated with C42_1 and 9 other fieldsHigh correlation
C42_1 is highly correlated with C42_2 and 8 other fieldsHigh correlation
C42_2 is highly correlated with C42_1High correlation
C42_3 is highly correlated with C42_1High correlation
C42B_1 is highly correlated with LNCAP_2High correlation
C42B_2 is highly correlated with C42_1High correlation
C42B_3 is highly correlated with C42_1 and 2 other fieldsHigh correlation
LNCAP_1 is highly correlated with C42_1 and 2 other fieldsHigh correlation
LNCAP_2 is highly correlated with C42_1 and 3 other fieldsHigh correlation
MR49F_1 is highly correlated with C42_1 and 4 other fieldsHigh correlation
MR49F_2 is highly correlated with C42_1 and 2 other fieldsHigh correlation
MR49F_3 is highly correlated with C42_1 and 4 other fieldsHigh correlation
C42B_2 is highly correlated with C42B_1 and 10 other fieldsHigh correlation
C42B_1 is highly correlated with C42B_2 and 9 other fieldsHigh correlation
MR49F_3 is highly correlated with C42B_2 and 9 other fieldsHigh correlation
C42_1 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
MR49F_2 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
LNCAP_2 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
LNCAP_1 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
LNCAP_3 is highly correlated with C42B_2 and 8 other fieldsHigh correlation
C42B_3 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
C42_3 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
MR49F_1 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
C42_2 is highly correlated with C42B_2 and 10 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
C42_2 has 155 (1.0%) zeros Zeros
C42_3 has 172 (1.1%) zeros Zeros
LNCAP_1 has 241 (1.6%) zeros Zeros
LNCAP_2 has 262 (1.7%) zeros Zeros
LNCAP_3 has 288 (1.9%) zeros Zeros
MR49F_1 has 277 (1.8%) zeros Zeros
MR49F_2 has 308 (2.0%) zeros Zeros
MR49F_3 has 320 (2.1%) zeros Zeros

Reproduction

Analysis started2021-09-03 02:26:21.811386
Analysis finished2021-09-03 02:27:03.007992
Duration41.2 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct15447
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size120.8 KiB
ENSG00000000003
 
1
ENSG00000179833
 
1
ENSG00000180104
 
1
ENSG00000180155
 
1
ENSG00000180178
 
1
Other values (15442)
15442 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters231705
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15447 ?
Unique (%)100.0%

Sample

1st rowENSG00000000003
2nd rowENSG00000000419
3rd rowENSG00000000457
4th rowENSG00000000460
5th rowENSG00000001036

Common Values

ValueCountFrequency (%)
ENSG000000000031
 
< 0.1%
ENSG000001798331
 
< 0.1%
ENSG000001801041
 
< 0.1%
ENSG000001801551
 
< 0.1%
ENSG000001801781
 
< 0.1%
ENSG000001801821
 
< 0.1%
ENSG000001801851
 
< 0.1%
ENSG000001801901
 
< 0.1%
ENSG000001801981
 
< 0.1%
ENSG000001802111
 
< 0.1%
Other values (15437)15437
99.9%

Length

2021-09-02T22:27:03.265863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ensg000000000031
 
< 0.1%
ensg000001798331
 
< 0.1%
ensg000001801041
 
< 0.1%
ensg000001801551
 
< 0.1%
ensg000001801781
 
< 0.1%
ensg000001801821
 
< 0.1%
ensg000001801851
 
< 0.1%
ensg000001801901
 
< 0.1%
ensg000001801981
 
< 0.1%
ensg000001802111
 
< 0.1%
Other values (15437)15437
99.9%

Most occurring characters

ValueCountFrequency (%)
086811
37.5%
117679
 
7.6%
E15447
 
6.7%
N15447
 
6.7%
S15447
 
6.7%
G15447
 
6.7%
210870
 
4.7%
68508
 
3.7%
38143
 
3.5%
78058
 
3.5%
Other values (4)29848
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number169917
73.3%
Uppercase Letter61788
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
086811
51.1%
117679
 
10.4%
210870
 
6.4%
68508
 
5.0%
38143
 
4.8%
78058
 
4.7%
47836
 
4.6%
57633
 
4.5%
87623
 
4.5%
96756
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
E15447
25.0%
N15447
25.0%
S15447
25.0%
G15447
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common169917
73.3%
Latin61788
 
26.7%

Most frequent character per script

Common
ValueCountFrequency (%)
086811
51.1%
117679
 
10.4%
210870
 
6.4%
68508
 
5.0%
38143
 
4.8%
78058
 
4.7%
47836
 
4.6%
57633
 
4.5%
87623
 
4.5%
96756
 
4.0%
Latin
ValueCountFrequency (%)
E15447
25.0%
N15447
25.0%
S15447
25.0%
G15447
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII231705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
086811
37.5%
117679
 
7.6%
E15447
 
6.7%
N15447
 
6.7%
S15447
 
6.7%
G15447
 
6.7%
210870
 
4.7%
68508
 
3.7%
38143
 
3.5%
78058
 
3.5%
Other values (4)29848
 
12.9%

C42_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15369
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.951898787
Minimum0
Maximum7.892393394
Zeros61
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:03.382724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1610274862
Q10.8498795217
median1.862615638
Q32.83823344
95-th percentile4.23297546
Maximum7.892393394
Range7.892393394
Interquartile range (IQR)1.988353918

Descriptive statistics

Standard deviation1.300086919
Coefficient of variation (CV)0.6660626705
Kurtosis-0.1677060908
Mean1.951898787
Median Absolute Deviation (MAD)0.9978378567
Skewness0.5317600109
Sum30150.98057
Variance1.690225997
MonotonicityNot monotonic
2021-09-02T22:27:03.520539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061
 
0.4%
0.66594041012
 
< 0.1%
1.2818464842
 
< 0.1%
1.416809962
 
< 0.1%
4.0284340772
 
< 0.1%
0.8988861472
 
< 0.1%
0.30263376182
 
< 0.1%
3.2272631582
 
< 0.1%
0.90080382642
 
< 0.1%
2.4016380972
 
< 0.1%
Other values (15359)15368
99.5%
ValueCountFrequency (%)
061
0.4%
0.00050850170781
 
< 0.1%
0.0015730132681
 
< 0.1%
0.0015977932251
 
< 0.1%
0.001873960041
 
< 0.1%
0.0021407216681
 
< 0.1%
0.0023309043311
 
< 0.1%
0.0029565221781
 
< 0.1%
0.002986672451
 
< 0.1%
0.0031382927211
 
< 0.1%
ValueCountFrequency (%)
7.8923933941
< 0.1%
7.8319875931
< 0.1%
7.6199866921
< 0.1%
7.392502191
< 0.1%
7.3796776911
< 0.1%
7.2732292971
< 0.1%
7.0142931011
< 0.1%
6.9981748981
< 0.1%
6.9040188071
< 0.1%
6.8512514851
< 0.1%

C42_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14653
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.552723153
Minimum0
Maximum10.43157052
Zeros155
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:03.663585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5061604513
Q11.95704364
median3.406321311
Q35.024928624
95-th percentile6.996169952
Maximum10.43157052
Range10.43157052
Interquartile range (IQR)3.067884984

Descriptive statistics

Standard deviation2.012930941
Coefficient of variation (CV)0.5665881789
Kurtosis-0.6951380179
Mean3.552723153
Median Absolute Deviation (MAD)1.519974044
Skewness0.2834276573
Sum54878.91454
Variance4.051890974
MonotonicityNot monotonic
2021-09-02T22:27:03.799031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0155
 
1.0%
4.846817387
 
< 0.1%
2.7192112636
 
< 0.1%
2.4532667855
 
< 0.1%
3.2683094925
 
< 0.1%
5.940180365
 
< 0.1%
1.7444554984
 
< 0.1%
2.8208913194
 
< 0.1%
1.9481829864
 
< 0.1%
4.2690786794
 
< 0.1%
Other values (14643)15248
98.7%
ValueCountFrequency (%)
0155
1.0%
0.0090361091191
 
< 0.1%
0.0095418315481
 
< 0.1%
0.0099687418911
 
< 0.1%
0.010646713281
 
< 0.1%
0.017813712431
 
< 0.1%
0.020139550921
 
< 0.1%
0.020524001161
 
< 0.1%
0.020847454771
 
< 0.1%
0.023172462631
 
< 0.1%
ValueCountFrequency (%)
10.431570521
< 0.1%
10.3496531
< 0.1%
10.28217191
< 0.1%
9.9409163691
< 0.1%
9.7306906911
< 0.1%
9.7198213341
< 0.1%
9.7187460111
< 0.1%
9.70228891
< 0.1%
9.5717229061
< 0.1%
9.5267403591
< 0.1%

C42_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14692
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.539029987
Minimum0
Maximum10.3760024
Zeros172
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:03.929398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6003213661
Q12.11978603
median3.592985744
Q34.869818591
95-th percentile6.45859939
Maximum10.3760024
Range10.3760024
Interquartile range (IQR)2.750032561

Descriptive statistics

Standard deviation1.809643457
Coefficient of variation (CV)0.511338831
Kurtosis-0.6145670019
Mean3.539029987
Median Absolute Deviation (MAD)1.366996069
Skewness0.07097125064
Sum54667.39621
Variance3.27480944
MonotonicityNot monotonic
2021-09-02T22:27:04.059362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
 
1.1%
4.9513598498
 
0.1%
3.0470164085
 
< 0.1%
2.9202478725
 
< 0.1%
1.7939420865
 
< 0.1%
5.6409638685
 
< 0.1%
2.7104943535
 
< 0.1%
2.0391592454
 
< 0.1%
3.0470164684
 
< 0.1%
3.4717391744
 
< 0.1%
Other values (14682)15230
98.6%
ValueCountFrequency (%)
0172
1.1%
0.014174171431
 
< 0.1%
0.022405240361
 
< 0.1%
0.025410926921
 
< 0.1%
0.036151353641
 
< 0.1%
0.040041781121
 
< 0.1%
0.04429848351
 
< 0.1%
0.046701269661
 
< 0.1%
0.047815069871
 
< 0.1%
0.048325479461
 
< 0.1%
ValueCountFrequency (%)
10.37600241
< 0.1%
10.13298091
< 0.1%
9.9705199531
< 0.1%
9.865495631
< 0.1%
9.7939205721
< 0.1%
9.5134508131
< 0.1%
9.4848072571
< 0.1%
9.4527539221
< 0.1%
9.3858018611
< 0.1%
9.2538389721
< 0.1%

C42B_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14928
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.370020893
Minimum0
Maximum10.3141713
Zeros145
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:04.189608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2221046035
Q11.585332705
median3.505783856
Q34.935459095
95-th percentile6.650378693
Maximum10.3141713
Range10.3141713
Interquartile range (IQR)3.35012639

Descriptive statistics

Standard deviation2.059899841
Coefficient of variation (CV)0.611242454
Kurtosis-0.8741049554
Mean3.370020893
Median Absolute Deviation (MAD)1.630820221
Skewness0.07756979364
Sum52056.71274
Variance4.243187354
MonotonicityNot monotonic
2021-09-02T22:27:04.323137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0145
 
0.9%
4.7568957429
 
0.1%
5.8497634029
 
0.1%
4.0723040996
 
< 0.1%
5.4457375486
 
< 0.1%
2.7358605375
 
< 0.1%
3.5253859195
 
< 0.1%
2.9312682395
 
< 0.1%
3.6753219524
 
< 0.1%
2.2237280034
 
< 0.1%
Other values (14918)15249
98.7%
ValueCountFrequency (%)
0145
0.9%
0.0069791748031
 
< 0.1%
0.0073999159621
 
< 0.1%
0.010567674911
 
< 0.1%
0.010743808051
 
< 0.1%
0.011463225451
 
< 0.1%
0.013829115461
 
< 0.1%
0.014115022711
 
< 0.1%
0.014525502241
 
< 0.1%
0.014946995031
 
< 0.1%
ValueCountFrequency (%)
10.31417131
< 0.1%
10.173305791
< 0.1%
10.058810091
< 0.1%
9.9958406371
< 0.1%
9.901171741
< 0.1%
9.5589781971
< 0.1%
9.4821848061
< 0.1%
9.4699220561
< 0.1%
9.3156575871
< 0.1%
9.314756541
< 0.1%

C42B_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15135
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.150177325
Minimum0
Maximum9.07493053
Zeros151
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:04.454201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1886488344
Q11.031215756
median2.083637162
Q33.070509477
95-th percentile4.499787722
Maximum9.07493053
Range9.07493053
Interquartile range (IQR)2.039293721

Descriptive statistics

Standard deviation1.361662853
Coefficient of variation (CV)0.6332793286
Kurtosis-0.05635037148
Mean2.150177325
Median Absolute Deviation (MAD)1.015766542
Skewness0.4862304588
Sum33213.78914
Variance1.854125725
MonotonicityNot monotonic
2021-09-02T22:27:04.579243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0151
 
1.0%
3.2899827278
 
0.1%
2.9683024936
 
< 0.1%
2.8153284774
 
< 0.1%
2.5177819533
 
< 0.1%
3.7857944823
 
< 0.1%
3.0572294563
 
< 0.1%
1.6966402213
 
< 0.1%
3.0741038933
 
< 0.1%
2.271812743
 
< 0.1%
Other values (15125)15260
98.8%
ValueCountFrequency (%)
0151
1.0%
0.0069880108791
 
< 0.1%
0.0092635023931
 
< 0.1%
0.0097699151821
 
< 0.1%
0.010398943081
 
< 0.1%
0.010468875141
 
< 0.1%
0.010831311971
 
< 0.1%
0.01099256941
 
< 0.1%
0.012230804611
 
< 0.1%
0.013458940751
 
< 0.1%
ValueCountFrequency (%)
9.074930531
< 0.1%
8.3405171091
< 0.1%
8.2112436811
< 0.1%
8.1105906371
< 0.1%
8.0470522731
< 0.1%
7.9594411611
< 0.1%
7.6585334281
< 0.1%
7.6479992861
< 0.1%
7.6324766461
< 0.1%
7.5871028211
< 0.1%

C42B_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14709
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.688297728
Minimum0
Maximum12.08922249
Zeros145
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:04.704019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6114117762
Q12.118754437
median3.684648275
Q35.122908431
95-th percentile6.9340729
Maximum12.08922249
Range12.08922249
Interquartile range (IQR)3.004153993

Descriptive statistics

Standard deviation1.959593808
Coefficient of variation (CV)0.5313003321
Kurtosis-0.7047934925
Mean3.688297728
Median Absolute Deviation (MAD)1.509203776
Skewness0.1492733792
Sum56973.13501
Variance3.840007892
MonotonicityNot monotonic
2021-09-02T22:27:04.839628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0145
 
0.9%
4.0439607438
 
0.1%
4.7283053397
 
< 0.1%
2.6524440276
 
< 0.1%
3.2227508235
 
< 0.1%
4.3272512095
 
< 0.1%
3.1536221885
 
< 0.1%
3.3681896325
 
< 0.1%
5.6392770855
 
< 0.1%
1.8466063344
 
< 0.1%
Other values (14699)15252
98.7%
ValueCountFrequency (%)
0145
0.9%
0.0089855735481
 
< 0.1%
0.0091896089561
 
< 0.1%
0.014813029721
 
< 0.1%
0.017073646831
 
< 0.1%
0.024202924091
 
< 0.1%
0.025630670041
 
< 0.1%
0.029263954521
 
< 0.1%
0.029510480691
 
< 0.1%
0.029531216791
 
< 0.1%
ValueCountFrequency (%)
12.089222491
< 0.1%
11.222503641
< 0.1%
10.276501551
< 0.1%
10.149076721
< 0.1%
9.9099514651
< 0.1%
9.6892777261
< 0.1%
9.6841310841
< 0.1%
9.5803889881
< 0.1%
9.4788567421
< 0.1%
9.4168887111
< 0.1%

LNCAP_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14741
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.604673585
Minimum0
Maximum10.71102758
Zeros241
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:04.971619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.457849735
Q12.205752864
median3.691843215
Q34.963779829
95-th percentile6.56451032
Maximum10.71102758
Range10.71102758
Interquartile range (IQR)2.758026965

Descriptive statistics

Standard deviation1.864446435
Coefficient of variation (CV)0.5172303098
Kurtosis-0.5852331016
Mean3.604673585
Median Absolute Deviation (MAD)1.366280369
Skewness0.01355618853
Sum55681.39286
Variance3.47616051
MonotonicityNot monotonic
2021-09-02T22:27:05.101750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0241
 
1.6%
3.641561197
 
< 0.1%
3.1480588626
 
< 0.1%
2.1928822745
 
< 0.1%
1.84171345
 
< 0.1%
5.815211044
 
< 0.1%
3.6415611444
 
< 0.1%
3.491769564
 
< 0.1%
2.13182884
 
< 0.1%
1.0287783684
 
< 0.1%
Other values (14731)15163
98.2%
ValueCountFrequency (%)
0241
1.6%
0.0072060846931
 
< 0.1%
0.011672270061
 
< 0.1%
0.013852795431
 
< 0.1%
0.014889154371
 
< 0.1%
0.015429191311
 
< 0.1%
0.017436314211
 
< 0.1%
0.018396141281
 
< 0.1%
0.021626756461
 
< 0.1%
0.021885818691
 
< 0.1%
ValueCountFrequency (%)
10.711027581
< 0.1%
10.011732721
< 0.1%
9.8978805241
< 0.1%
9.8133418141
< 0.1%
9.7408356731
< 0.1%
9.6276223751
< 0.1%
9.565186861
< 0.1%
9.2889121521
< 0.1%
9.2073732821
< 0.1%
9.2026340081
< 0.1%

LNCAP_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14898
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.970001739
Minimum0
Maximum11.0673857
Zeros262
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:05.231506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1440552365
Q11.090400324
median2.83238737
Q34.57855383
95-th percentile6.52172794
Maximum11.0673857
Range11.0673857
Interquartile range (IQR)3.488153506

Descriptive statistics

Standard deviation2.069878367
Coefficient of variation (CV)0.6969283349
Kurtosis-0.8115342857
Mean2.970001739
Median Absolute Deviation (MAD)1.742738052
Skewness0.3505866418
Sum45877.61687
Variance4.284396454
MonotonicityNot monotonic
2021-09-02T22:27:05.355447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0262
 
1.7%
4.7704720676
 
< 0.1%
3.4092888985
 
< 0.1%
2.6920397425
 
< 0.1%
2.2359485325
 
< 0.1%
4.5898443514
 
< 0.1%
5.8634174114
 
< 0.1%
5.297603943
 
< 0.1%
4.5534921973
 
< 0.1%
1.5504285163
 
< 0.1%
Other values (14888)15147
98.1%
ValueCountFrequency (%)
0262
1.7%
0.0059193549991
 
< 0.1%
0.0067267863561
 
< 0.1%
0.010102866741
 
< 0.1%
0.010645326911
 
< 0.1%
0.011309161261
 
< 0.1%
0.011491365221
 
< 0.1%
0.012042671091
 
< 0.1%
0.012396178911
 
< 0.1%
0.012879353911
 
< 0.1%
ValueCountFrequency (%)
11.06738571
< 0.1%
9.8722891481
< 0.1%
9.7753930271
< 0.1%
9.6761716061
< 0.1%
9.385147181
< 0.1%
9.3387039111
< 0.1%
9.2877393351
< 0.1%
9.2585703791
< 0.1%
9.1481229491
< 0.1%
9.0921711341
< 0.1%

LNCAP_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14344
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.573745194
Minimum0
Maximum12.00651744
Zeros288
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:05.477927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1510752765
Q11.42177245
median2.537786457
Q33.554847892
95-th percentile5.324498014
Maximum12.00651744
Range12.00651744
Interquartile range (IQR)2.133075442

Descriptive statistics

Standard deviation1.570222596
Coefficient of variation (CV)0.610092483
Kurtosis0.2561218642
Mean2.573745194
Median Absolute Deviation (MAD)1.066457613
Skewness0.4874070141
Sum39756.64201
Variance2.465599001
MonotonicityNot monotonic
2021-09-02T22:27:05.610653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0288
 
1.9%
2.98326133413
 
0.1%
2.93995420112
 
0.1%
2.66683785312
 
0.1%
3.04887343511
 
0.1%
2.91304010110
 
0.1%
2.75581253610
 
0.1%
2.33950161510
 
0.1%
3.60631164410
 
0.1%
2.6134369129
 
0.1%
Other values (14334)15062
97.5%
ValueCountFrequency (%)
0288
1.9%
0.0038815221371
 
< 0.1%
0.0093529147891
 
< 0.1%
0.0097619056451
 
< 0.1%
0.010320776471
 
< 0.1%
0.010353582461
 
< 0.1%
0.010397901051
 
< 0.1%
0.010822692811
 
< 0.1%
0.010953381631
 
< 0.1%
0.011245600281
 
< 0.1%
ValueCountFrequency (%)
12.006517441
< 0.1%
10.595120091
< 0.1%
10.068557821
< 0.1%
9.6756034071
< 0.1%
9.6743183841
< 0.1%
9.6198426531
< 0.1%
9.3857732561
< 0.1%
8.8945394441
< 0.1%
8.7383222351
< 0.1%
8.6417247821
< 0.1%

MR49F_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14677
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.458925762
Minimum0
Maximum11.92862799
Zeros277
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:05.736106image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3672982593
Q12.01226665
median3.544871663
Q34.795379487
95-th percentile6.480920216
Maximum11.92862799
Range11.92862799
Interquartile range (IQR)2.783112837

Descriptive statistics

Standard deviation1.864869199
Coefficient of variation (CV)0.5391469282
Kurtosis-0.5512277302
Mean3.458925762
Median Absolute Deviation (MAD)1.389315832
Skewness0.09543331641
Sum53430.02624
Variance3.47773713
MonotonicityNot monotonic
2021-09-02T22:27:05.883812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0277
 
1.8%
3.6004247717
 
< 0.1%
2.5898558316
 
< 0.1%
3.3218055466
 
< 0.1%
1.600818275
 
< 0.1%
4.6806611745
 
< 0.1%
1.7308190894
 
< 0.1%
3.5685449384
 
< 0.1%
2.7888956634
 
< 0.1%
2.2928827774
 
< 0.1%
Other values (14667)15125
97.9%
ValueCountFrequency (%)
0277
1.8%
0.0037435143941
 
< 0.1%
0.016748733611
 
< 0.1%
0.016960910791
 
< 0.1%
0.017279112581
 
< 0.1%
0.019939623481
 
< 0.1%
0.022019044661
 
< 0.1%
0.022374114271
 
< 0.1%
0.022406958571
 
< 0.1%
0.02345646561
 
< 0.1%
ValueCountFrequency (%)
11.928627991
< 0.1%
10.755410091
< 0.1%
10.312028631
< 0.1%
9.9446405321
< 0.1%
9.8273914961
< 0.1%
9.7779354311
< 0.1%
9.7698157571
< 0.1%
9.7005361611
< 0.1%
9.6421189251
< 0.1%
9.6285459741
< 0.1%

MR49F_2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14579
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.505392333
Minimum0
Maximum10.39896276
Zeros308
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:06.016956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3856645838
Q12.008680044
median3.571742366
Q34.907524339
95-th percentile6.643014171
Maximum10.39896276
Range10.39896276
Interquartile range (IQR)2.898844295

Descriptive statistics

Standard deviation1.912064336
Coefficient of variation (CV)0.5454637184
Kurtosis-0.6707650258
Mean3.505392333
Median Absolute Deviation (MAD)1.443700972
Skewness0.08612470762
Sum54147.79536
Variance3.655990026
MonotonicityNot monotonic
2021-09-02T22:27:06.146385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0308
 
2.0%
4.0820159536
 
< 0.1%
4.7666906356
 
< 0.1%
6.146583185
 
< 0.1%
2.6353174885
 
< 0.1%
0.99830233085
 
< 0.1%
1.4027998225
 
< 0.1%
3.0165935655
 
< 0.1%
1.0270317865
 
< 0.1%
4.4818407125
 
< 0.1%
Other values (14569)15092
97.7%
ValueCountFrequency (%)
0308
2.0%
0.015599448611
 
< 0.1%
0.016302174521
 
< 0.1%
0.017562620351
 
< 0.1%
0.020679383391
 
< 0.1%
0.022396423011
 
< 0.1%
0.02286669371
 
< 0.1%
0.023203650981
 
< 0.1%
0.023331415861
 
< 0.1%
0.024221353521
 
< 0.1%
ValueCountFrequency (%)
10.398962761
< 0.1%
10.104953841
< 0.1%
9.9357553191
< 0.1%
9.9184372471
< 0.1%
9.8643785451
< 0.1%
9.8481598151
< 0.1%
9.844957081
< 0.1%
9.7090747911
< 0.1%
9.7073429731
< 0.1%
9.7031403051
< 0.1%

MR49F_3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct14957
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.605250162
Minimum0
Maximum10.6228612
Zeros320
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size120.8 KiB
2021-09-02T22:27:06.275196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09483151545
Q10.8508791566
median2.329808257
Q33.939173166
95-th percentile6.369273736
Maximum10.6228612
Range10.6228612
Interquartile range (IQR)3.08829401

Descriptive statistics

Standard deviation1.993926657
Coefficient of variation (CV)0.7653494032
Kurtosis-0.3322920922
Mean2.605250162
Median Absolute Deviation (MAD)1.529473531
Skewness0.6420145042
Sum40243.29926
Variance3.975743514
MonotonicityNot monotonic
2021-09-02T22:27:06.406142image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0320
 
2.1%
0.30082766435
 
< 0.1%
3.1554032344
 
< 0.1%
4.7301493144
 
< 0.1%
4.0457885733
 
< 0.1%
2.1385943383
 
< 0.1%
1.8091265743
 
< 0.1%
0.86985879373
 
< 0.1%
2.5039939913
 
< 0.1%
1.9637690523
 
< 0.1%
Other values (14947)15096
97.7%
ValueCountFrequency (%)
0320
2.1%
0.0047461101071
 
< 0.1%
0.0057672742561
 
< 0.1%
0.0064654804761
 
< 0.1%
0.0075175257471
 
< 0.1%
0.0077131949721
 
< 0.1%
0.0085466729021
 
< 0.1%
0.0095211801571
 
< 0.1%
0.0097273376751
 
< 0.1%
0.010339888631
 
< 0.1%
ValueCountFrequency (%)
10.62286121
< 0.1%
9.5097938081
< 0.1%
9.4092008491
< 0.1%
9.3167493641
< 0.1%
9.2947652991
< 0.1%
9.225225451
< 0.1%
9.197279081
< 0.1%
9.1969836731
< 0.1%
9.1819392731
< 0.1%
9.1819103741
< 0.1%

Interactions

2021-09-02T22:26:44.041174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:44.204090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:44.330184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:44.495321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:44.675535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:44.811985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:44.986156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:45.115250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:45.241050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:45.369983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:45.485832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:45.598825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:45.710093image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:45.818496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:45.937365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:46.118651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:46.331574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:46.478931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:46.589663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:46.699084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:46.802589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:46.908961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:47.022360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:47.130427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:47.232454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:47.340676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:47.443364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:47.545878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:47.648834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:48.679299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:48.824964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:48.958137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:49.100088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:49.220164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:49.346885image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:49.475834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:49.602463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:49.723289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:49.835157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:49.955168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:50.105569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:50.242195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:50.345620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:50.447062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:50.551002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:50.702286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:50.916433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:51.098816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:51.254504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:51.456725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:51.602226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:51.703474image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:51.800582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:51.895316image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:52.024071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:52.225506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:52.502434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:52.684049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:52.802804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:52.912450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:53.022082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:53.143304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:53.260932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:53.375083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:53.489849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:53.622212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:53.780146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:53.914613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:54.018838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:54.124485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:54.230695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:54.334118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:54.437906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:54.544706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:54.665922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:54.818058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:55.192866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:55.291538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:55.394447image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:55.500539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:55.608966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:55.716639image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:55.828185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:55.930673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.031448image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.139621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.240587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.342006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.443069image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.542432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.652874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.763737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.866119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:56.968765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.071564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.172565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.272941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.383758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.488307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.595080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.706960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.809366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:57.915549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.020231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.124378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.232181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.338196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.442573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.547712image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.658634image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.762703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.866714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:58.970831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.074150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.179987image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.286728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.391795image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.498318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.605963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.711354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.818527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:26:59.926210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.027477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.129364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.229809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.328399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.431693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.536839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.638703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.741888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.845731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:00.946392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:01.047352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:01.433033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:01.533001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:01.635814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:01.739737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:01.839163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:01.941762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:02.042393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:02.143249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:02.247464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:02.350131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-09-02T22:27:02.450552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-09-02T22:27:06.526282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-02T22:27:06.688771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-02T22:27:06.845640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-02T22:27:07.003617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-02T22:27:02.665741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-02T22:27:02.897598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0C42_1C42_2C42_3C42B_1C42B_2C42B_3LNCAP_1LNCAP_2LNCAP_3MR49F_1MR49F_2MR49F_3
0ENSG000000000032.0203081.7850152.3241273.1970072.0648426.4457623.2954972.6075192.2220213.1895404.8943412.940336
1ENSG000000004194.2233504.7139715.3146524.4879931.8927686.3120675.7413324.7329001.9279135.2837325.6328294.111696
2ENSG000000004572.8699191.3743232.9276704.7868541.6365815.3951423.2232213.6754301.5990963.7009284.1736793.952226
3ENSG000000004601.7519043.7263535.0715062.2020741.8974055.4281053.8052284.0568253.0830223.5775443.7050593.998118
4ENSG000000010363.4811975.0636556.1822333.1058993.8101567.6831524.7687225.2383803.1195614.6880144.7263665.203400
5ENSG000000010842.9423302.3334944.6177135.6350602.8963786.7751703.5982482.8528362.2223174.5632385.1611994.686099
6ENSG000000011672.2360614.3629504.7129834.5748764.1619596.5825904.6720794.1341033.8839995.3260484.4322333.775862
7ENSG000000014601.1858080.5588033.4822161.2339321.6587884.1827301.8963933.5367271.1892412.7664172.3791612.201291
8ENSG000000014612.1761794.4193523.6428025.0743643.1501586.2545125.1827304.6144922.2799974.5563804.5413124.362653
9ENSG000000014973.1808363.8079095.0777883.6105493.3875356.8776754.3565363.7146802.2849865.9669476.2629355.303243

Last rows

Unnamed: 0C42_1C42_2C42_3C42B_1C42B_2C42B_3LNCAP_1LNCAP_2LNCAP_3MR49F_1MR49F_2MR49F_3
15437ENSG000002834860.4780851.3041071.3180690.2925692.2463451.3043030.0000000.1871862.8080910.8740591.2544800.057845
15438ENSG000002834910.3723421.9872901.0748130.7783663.4790821.0979082.4564910.7839382.0979862.0764240.9904100.155038
15439ENSG000002834980.4127901.7985331.2834450.1991293.1511801.6346241.1629110.7816554.1140551.2243941.8374970.475062
15440ENSG000002835110.4415535.4030621.4725950.6579732.7402641.2851390.0676670.0225421.7539411.7308191.0259360.159346
15441ENSG000002835261.9763933.2887853.5690110.8118984.4163652.2065593.3433841.6031891.7983743.4442523.1578032.358798
15442ENSG000002835770.0000000.0000000.0000001.1853953.0387331.4079590.0000000.0000000.0000000.2410260.4431190.057711
15443ENSG000002836330.0000000.0000000.0000001.2814483.8623372.7778430.0000000.0000000.0000000.0000000.0000000.000000
15444ENSG000002836670.2842460.5898150.5170130.2816622.2056060.1998810.7477850.0978022.3565671.5113731.5382551.308785
15445ENSG000002836741.2872841.5006341.1496042.1124273.7120652.0683371.6488800.9048981.4827421.4921161.9569220.306116
15446ENSG000002836890.2916080.5772021.4123190.6430743.0387331.6468870.6091680.5004721.2582601.0041870.9072080.402196